Feature selection and feature transformation are very important components in any neural activity classification pipeline. Generally, neural activity data is very noisy. One source of noise comes from the technology used to extract the neural signals. This can be signal drift caused by, for example, the device for measuring neural activity “warming-up” over time, inhomogeneities of a magnetic field used to measure the signals, variance in conductance of any electrodes receiving signals, and general noise introduced by electronic circuits.
Another source of noise is physiologically based. This includes, for example, fluctuations caused by movement, variation in heart rate, changes in skin resistance, and blinking of the subject. Finally, the most complex form of noise is random off-task mental activity by the subject. Given this complexity and the high dimensionality of neural activity signals, classification of brain states via these signals is difficult and error prone.
Numerous methods have been developed to deal with this difficulty. Given the large number of data dimensions available, one class of techniques tries to determine which dimensions are more informative and throw the others out, thereby reducing the dimensionality of the problem. Selecting “stable” voxels (a single data point on a regularly spaced, three-dimensional grid), as described in Literature Reference No. 3 of the List of Incorporated Cited Literature References, is one such technique. Another technique is using some discriminant measure to rank voxels.
In addition to reducing the number of dimensions, transformations over the representation space has also been performed. A common method for this is using Singular Value Decomposition (SVD) to transform the data (see Literature Reference No. 4 for a description of SVD). The SVD method prioritizes data according to variance, but may not correlate with discriminability. Another method is linear regression to extract the beta coefficients as an alternative representation of the data, as described in Literature Reference No. 2.
While the prior methods described above are able to reduce the dimensionality of neural activity data, they are not designed to distinguish neural activity patterns. Because of the high dimensionality of the data (due to noise and over abundant measurements) and the actual information, content is embedded within a much lower dimensional manifold. Thus, a continuing need exists for a system for accessing the content via sparse and low-rank (SLR) decomposition to find the representation of the signal content amongst the high-dimensional data signal.